1. Field of the Invention
The present invention relates to a method and apparatus for characterizing atrial arrhythmias and in particular to a method and apparatus employing time-frequency analysis of electrical signals originating from the atria in order to derive indicia for use in characterizing the arrhythmias.
2. Description of the Prior Art
It is desirable to find methods, particularly non-invasive methods, for the characterization and classification of atrial arrhythmias, including tachycardia, flutter and fibrillation. Information contained in the atrial activity must, in some suitable way, be quantified to accomplish this task. To date, the primary efforts in this field have been directed toward atrial fibrillation analysis although the same methods in many cases can be used for flutter and tachycardia. In the atrial fibrillation case, the atrial activity in an electrocardiogram (ECG), registered from either internal or external of the patient, has conventionally been classified by its maximum amplitude. The repetition rate (or atrial cycle length) of the f-waves in the ECG has also been investigated and serves as an index of the degree of atrial organization. Estimation of the average repetition rate can be based on spectral analysis. Such an approach gives a general picture of the signal by providing information about the average repetition rate by means of the peak location, the variation in the rate by the width of the peak and the average signal energy by the peak amplitude. This method is simple but provides valuable clinical information.
Atrial signals may be non-stationary but are repetitive and thus they can, during short intervals, be represented by a fundamental frequency signal, which reflects the repetition rate and a harmonic pattern which reflects the shape of the fibrillatory waveform. Based on this recognition a detailed feature extraction in the time-frequency plane for this type of signal may be achieved.
Time-frequency analysis (TFA) using an iterative cross-Wigner-Ville distribution (XWVD) to analyze the signals on a second-to-second basis is known and can provide a more detailed temporal characterization of variations in the repetition rate than the above mentioned approaches.
The XWVD models the frequency variations as a frequency-modulated sinusoid, which has a low-pass effect on the trends. Further, it only uses the energy in the fundamental frequency and is therefore not capable of tracking the shape of the signals as described by its harmonics. Another limitation is that, because of the large number of iterations performed, the computational complexity is relatively high.